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KemptenCity - Semantic Segmentation of Urban Areas for Simulation

  • Autonomous driving and traffic flow simulation requires a realistic and accurate representation of the environment. Therefore, this research focuses on the semantic segmentation of aerial images for simulation purposes. Initially, a dataset was created based on true orthophotos from 2019 and Kempten’s street cadaster, with true orthophotos being fully rectified aerial images. The chosen classes were oriented towards the subsequent conversion and usage in simulation. The proposed labeling workflow used cadaster data and demonstrated significant time efficiency compared to state-of-the-art datasets. Subsequently, a neural network was implemented that was trained and tested on the dataset. In addition, the network was also trained only on the lane markings to compare the network’s performance. Both cases demonstrated excellent segmentation results. The generalizability was then tested on true orthophotos from 2021. The results indicated a solid generalizability, but still needs to be improved. Finally, the aerial information wasAutonomous driving and traffic flow simulation requires a realistic and accurate representation of the environment. Therefore, this research focuses on the semantic segmentation of aerial images for simulation purposes. Initially, a dataset was created based on true orthophotos from 2019 and Kempten’s street cadaster, with true orthophotos being fully rectified aerial images. The chosen classes were oriented towards the subsequent conversion and usage in simulation. The proposed labeling workflow used cadaster data and demonstrated significant time efficiency compared to state-of-the-art datasets. Subsequently, a neural network was implemented that was trained and tested on the dataset. In addition, the network was also trained only on the lane markings to compare the network’s performance. Both cases demonstrated excellent segmentation results. The generalizability was then tested on true orthophotos from 2021. The results indicated a solid generalizability, but still needs to be improved. Finally, the aerial information was converted into a 3D environment, that can be used in simulations. Our results confirm the usage of aerial imagery and street cadaster data as a basis for the simulations.show moreshow less

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Metadaten
Author:Peter Buckel, Stefan-Alexander SchneiderORCiDGND, Jürgen StübnerORCiD, Florian Frank
URL / DOI:https://isprs-annals.copernicus.org/articles/X-4-W5-2024/71/2024/
DOI:https://doi.org/10.5194/isprs-annals-X-4-W5-2024-71-2024
Identifier:2194-9042 OPAC HS OPAC extern
Identifier:2194-9050 OPAC HS OPAC extern
Parent Title (English):ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publisher:Copernicus Publications
Place of publication:Katlenburg-Lindau
Document Type:conference proceeding (article)
Conference:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume X-4/W5-2024 19th 3D GeoInfo Conference 2024, 1–3 July 2024, Vigo, Spain
Language:English
Date of Publication (online):2024/06/27
Year of first Publication:2024
Tag:Deep Learning; Digital Twin; Geoinformation Data; Remote Sensing; Simulation
Volume:2024
Issue:X-4/WS-2024
Number of pages:8 Seiten
First Page:71
Last Page:78
Institutes:Fakultät Elektrotechnik
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie
Open Access:open_access
Research focus:FSP2: Mobilität
Publication Lists:Schneider, Stefan-Alexander
Buckel, Peter
Stübner, Jürgen
Publication reviewed:begutachtet
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
Release Date:2024/07/08
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